Evolution through segmental duplications and losses: A super-reconciliation approach

Delabre, Matteo, El-Mabrouk, Nadia, Huber, Katharina, Lafond, Manuel, Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435, Noutahi, Emmanuel and Sautie Castellanos, Miguel (2020) Evolution through segmental duplications and losses: A super-reconciliation approach. Algorithms for Molecular Biology, 15 (1). ISSN 1748-7188

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The classical gene and species tree reconciliation, used to infer the history of gene gain and loss explaining the evolution of gene families, assumes an independent evolution for each family. While this assumption is reasonable for genes that are far apart in the genome, it is not appropriate for genes grouped into syntenic blocks, which are more plausibly the result of a concerted evolution. Here, we introduce the Super-Reconciliation problem which consists in inferring a history of segmental duplication and loss events (involving a set of neighboring genes) leading to a set of present-day syntenies from a single ancestral one. In other words, we extend the traditional Duplication-Loss reconciliation problem of a single gene tree, to a set of trees, accounting for segmental duplications and losses. Existency of a Super-Reconciliation depends on individual gene tree consistency. In addition, ignoring rearrangements implies that existency also depends on gene order consistency. We first show that the problem of reconstructing a most parsimonious Super-Reconciliation, if any, is NP-hard and give an exact exponential-time algorithm to solve it. Alternatively, we show that accounting for rearrangements in the evolutionary model, but still only minimizing segmental duplication and loss events, leads to an exact polynomial-time algorithm. We finally assess time efficiency of the former exponential time algorithm for the Duplication-Loss model on simulated datasets, and give a proof of concept on the opioid receptor genes.

Item Type: Article
Uncontrolled Keywords: duplication,gene tree,loss,reconciliation,synteny,structural biology,molecular biology,computational theory and mathematics,applied mathematics ,/dk/atira/pure/subjectarea/asjc/1300/1315
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Computational Biology
Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
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Depositing User: LivePure Connector
Date Deposited: 14 Aug 2019 09:30
Last Modified: 04 Mar 2024 17:49
URI: https://ueaeprints.uea.ac.uk/id/eprint/71959
DOI: 10.1186/s13015-020-00171-4


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